| Operators |
add_samples_image_class_svm — Add training samples from an image to the training data of a support vector machine.
add_samples_image_class_svm(Image, ClassRegions : : SVMHandle : )
add_samples_image_class_svm adds training samples from the image Image to the support vector machine (SVM) given by SVMHandle. add_samples_image_class_svm is used to store the training samples before training a classifier for the pixel classification of multichannel images with classify_image_class_svm. add_samples_image_class_svm works analogously to add_sample_class_svm.
The image Image must have a number of channels equal to NumFeatures, as specified with create_class_svm. The training regions for the NumClasses pixel classes are passed in ClassRegions. Hence, ClassRegions must be a tuple containing NumClasses regions. The order of the regions in ClassRegions determines the class of the pixels. If there are no samples for a particular class in Image, an empty region must be passed at the position of the class in ClassRegions. With this mechanism it is possible to use multiple images to add training samples for all relevant classes to the SVM by calling add_samples_image_class_svm multiple times with the different images and suitably chosen regions.
The regions in ClassRegions should contain representative training samples for the respective classes. Hence, they need not cover the entire image. The regions in ClassRegions should not overlap each other, because this would lead to the fact that in the training data the samples from the overlapping areas would be assigned to multiple classes, which may lead to slower convergence of the training and a lower classification performance.
A further application of this operator is the automatic novelty detection, where, e.g., anomalies in color or texture can be detected. For this mode a training set that defines a sample region (e.g., skin regions for skin detection or samples of the correct texture) is passed to the SVMHandle, which is created in the Mode 'novelty-detection'. After training, regions that differ from the trained sample regions are detected (e.g., the rejection class for skin or errors in texture).
This operator modifies the state of the following input parameter:
The value of this parameter may not be shared across multiple threads without external synchronization.Training image.
Regions of the classes to be trained.
SVM handle.
If the parameters are valid add_samples_image_class_svm returns the value 2 (H_MSG_TRUE). If necessary, an exception is raised.
train_class_svm, write_samples_class_svm
classify_image_class_svm, add_sample_class_svm, clear_samples_class_svm, get_sample_num_class_svm, get_sample_class_svm, add_samples_image_class_mlp
Foundation
| Operators |